






import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler

class 		_snake_case	(	A__       ):






			def __init__(							self		,       a		,       a		,       a		,       a		,       )   ->							Tuple:
						super().__init__()
						SCREAMING_SNAKE_CASE							=			value_function
						SCREAMING_SNAKE_CASE							=			unet
						SCREAMING_SNAKE_CASE							=			scheduler
						SCREAMING_SNAKE_CASE							=			env
						SCREAMING_SNAKE_CASE							=			env.get_dataset()
						SCREAMING_SNAKE_CASE							=			{}
						for key in self.data.keys():
									try:
												SCREAMING_SNAKE_CASE							=			self.data[key].mean()
									except:  # noqa: E722
												pass
						SCREAMING_SNAKE_CASE							=			{}
						for key in self.data.keys():
									try:
												SCREAMING_SNAKE_CASE							=			self.data[key].std()
									except:  # noqa: E722
												pass
						SCREAMING_SNAKE_CASE							=			env.observation_space.shape[0]
						SCREAMING_SNAKE_CASE							=			env.action_space.shape[0]






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a)   ->							Tuple:
						return (x_in - self.means[key]) / self.stds[key]






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a)   ->							List[str]:
						return x_in * self.stds[key] + self.means[key]






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Optional[Any]:
						if type(a) is dict:
									return {k: self.to_torch(a) for k, v in x_in.items()}
						elif torch.is_tensor(a):
									return x_in.to(self.unet.device)
						return torch.tensor(a		,       device=self.unet.device)






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a)   ->							List[Any]:
						for key, val in cond.items():
									SCREAMING_SNAKE_CASE							=			val.clone()
						return x_in






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a		,       a)   ->							str:
						SCREAMING_SNAKE_CASE							=			x.shape[0]
						SCREAMING_SNAKE_CASE							=			None
						for i in tqdm.tqdm(self.scheduler.timesteps):
									# create batch of timesteps to pass into model
									SCREAMING_SNAKE_CASE							=			torch.full((batch_size,)		,       a		,       device=self.unet.device		,       dtype=torch.long)
									for _ in range(a):
												with torch.enable_grad():
															x.requires_grad_()

															# permute to match dimension for pre-trained models
															SCREAMING_SNAKE_CASE							=			self.value_function(x.permute(0		,       2		,       1)		,       a).sample
															SCREAMING_SNAKE_CASE							=			torch.autograd.grad([y.sum()]		,       [x])[0]

															SCREAMING_SNAKE_CASE							=			self.scheduler._get_variance(a)
															SCREAMING_SNAKE_CASE							=			torch.exp(0.5 * posterior_variance)
															SCREAMING_SNAKE_CASE							=			model_std * grad

												SCREAMING_SNAKE_CASE							=			0
												SCREAMING_SNAKE_CASE							=			x.detach()
												SCREAMING_SNAKE_CASE							=			x + scale * grad
												SCREAMING_SNAKE_CASE							=			self.reset_xa(a		,       a		,       self.action_dim)

									SCREAMING_SNAKE_CASE							=			self.unet(x.permute(0		,       2		,       1)		,       a).sample.permute(0		,       2		,       1)

									# TODO: verify deprecation of this kwarg
									SCREAMING_SNAKE_CASE							=			self.scheduler.step(a		,       a		,       a		,       predict_epsilon=a)['prev_sample']

									# apply conditions to the trajectory (set the initial state)
									SCREAMING_SNAKE_CASE							=			self.reset_xa(a		,       a		,       self.action_dim)
									SCREAMING_SNAKE_CASE							=			self.to_torch(a)
						return x, y







			def __call__(							self		,       a		,       a=64		,       a=32		,       a=2		,       a=0.1)   ->							Optional[Any]:
						# normalize the observations and create  batch dimension
						SCREAMING_SNAKE_CASE							=			self.normalize(a		,       'observations')
						SCREAMING_SNAKE_CASE							=			obs[None].repeat(a		,       axis=0)

						SCREAMING_SNAKE_CASE							=			{0: self.to_torch(a)}
						SCREAMING_SNAKE_CASE							=			(batch_size, planning_horizon, self.state_dim + self.action_dim)

						# generate initial noise and apply our conditions (to make the trajectories start at current state)
						SCREAMING_SNAKE_CASE							=			randn_tensor(a		,       device=self.unet.device)
						SCREAMING_SNAKE_CASE							=			self.reset_xa(a		,       a		,       self.action_dim)
						SCREAMING_SNAKE_CASE							=			self.to_torch(a)

						# run the diffusion process
						SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			self.run_diffusion(a		,       a		,       a		,       a)

						# sort output trajectories by value
						SCREAMING_SNAKE_CASE							=			y.argsort(0		,       descending=a).squeeze()
						SCREAMING_SNAKE_CASE							=			x[sorted_idx]
						SCREAMING_SNAKE_CASE							=			sorted_values[:, :, : self.action_dim]
						SCREAMING_SNAKE_CASE							=			actions.detach().cpu().numpy()
						SCREAMING_SNAKE_CASE							=			self.de_normalize(a		,       key='actions')

						# select the action with the highest value
						if y is not None:
									SCREAMING_SNAKE_CASE							=			0
						else:
									# if we didn't run value guiding, select a random action
									SCREAMING_SNAKE_CASE							=			np.random.randint(0		,       a)

						SCREAMING_SNAKE_CASE							=			denorm_actions[selected_index, 0]
						return denorm_actions





"""simple docstring"""



from typing import Union

import fire
import torch
from tqdm import tqdm



def 	_lowerCamelCase(	a   ,    a = "cpu"   ,    a = None				):
       __a       =    torch.load(a   ,    map_location=a				)
       for k, v in tqdm(state_dict.items()				):
              if not isinstance(a   ,    torch.Tensor				):
                     raise TypeError("FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin"				)
              __a       =    v.half()
       if save_path is None:  # overwrite src_path
              __a       =    src_path
       torch.save(a   ,    a				)


if __name__ == "__main__":
   fire.Fire(convert)
